{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choropleth classification schemes from PySAL for use with GeoPandas\n",
    "<img src=\"http://pysal.readthedocs.io/en/latest/_static/images/socal_3.jpg\" width=\"200\" align=\"right\" alt=\"PySAL image\" title=\"PySAL image\">\n",
    "PySAL is a [Spatial Analysis Library](), which packages fast spatial algorithms used in various fields. These include Exploratory spatial data analysis, spatial inequality analysis, spatial analysis on networks, spatial dynamics, and many more.\n",
    "\n",
    "It is used under the hood in geopandas when plotting measures with a set of colors. There are many ways to classify data into different bins, depending on a number of classification schemes.\n",
    "\n",
    "<img src=\"http://alumni.media.mit.edu/~tpminka/courses/36-350.2001/lectures/day11/boston-kmeans.png\" width=\"300\">\n",
    "\n",
    "For example, if we have 20 countries whose average annual temperature varies between 5C and 25C, we can classify them in 4 bins by:\n",
    "* Quantiles\n",
    "    - Separates the rows into equal parts, 5 countries per bin.\n",
    "* Equal Intervals\n",
    "    - Separates the measure's interval into equal parts, 5C per bin.\n",
    "* Natural Breaks (Fischer Jenks)\n",
    "    - This algorithm tries to split the rows into naturaly occurring clusters. The numbers per bin will depend on how the observations are located on the interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-15T21:29:37.736444Z",
     "start_time": "2017-12-15T21:29:37.716444Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-15T21:29:39.866422Z",
     "start_time": "2017-12-15T21:29:39.846422Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations, Attributes: (49, 21)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AREA</th>\n",
       "      <th>PERIMETER</th>\n",
       "      <th>COLUMBUS_</th>\n",
       "      <th>COLUMBUS_I</th>\n",
       "      <th>POLYID</th>\n",
       "      <th>NEIG</th>\n",
       "      <th>HOVAL</th>\n",
       "      <th>INC</th>\n",
       "      <th>CRIME</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>...</th>\n",
       "      <th>DISCBD</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>NSA</th>\n",
       "      <th>NSB</th>\n",
       "      <th>EW</th>\n",
       "      <th>CP</th>\n",
       "      <th>THOUS</th>\n",
       "      <th>NEIGNO</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.309441</td>\n",
       "      <td>2.440629</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>80.467003</td>\n",
       "      <td>19.531</td>\n",
       "      <td>15.725980</td>\n",
       "      <td>2.850747</td>\n",
       "      <td>...</td>\n",
       "      <td>5.03</td>\n",
       "      <td>38.799999</td>\n",
       "      <td>44.070000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>POLYGON ((8.624129295349121 14.23698043823242,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.259329</td>\n",
       "      <td>2.236939</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44.567001</td>\n",
       "      <td>21.232</td>\n",
       "      <td>18.801754</td>\n",
       "      <td>5.296720</td>\n",
       "      <td>...</td>\n",
       "      <td>4.27</td>\n",
       "      <td>35.619999</td>\n",
       "      <td>42.380001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>POLYGON ((8.252790451049805 14.23694038391113,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.192468</td>\n",
       "      <td>2.187547</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>26.350000</td>\n",
       "      <td>15.956</td>\n",
       "      <td>30.626781</td>\n",
       "      <td>4.534649</td>\n",
       "      <td>...</td>\n",
       "      <td>3.89</td>\n",
       "      <td>39.820000</td>\n",
       "      <td>41.180000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>POLYGON ((8.653305053710938 14.00809001922607,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.083841</td>\n",
       "      <td>1.427635</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>33.200001</td>\n",
       "      <td>4.477</td>\n",
       "      <td>32.387760</td>\n",
       "      <td>0.394427</td>\n",
       "      <td>...</td>\n",
       "      <td>3.70</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>40.520000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>POLYGON ((8.459499359130859 13.82034969329834,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.488888</td>\n",
       "      <td>2.997133</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>23.225000</td>\n",
       "      <td>11.252</td>\n",
       "      <td>50.731510</td>\n",
       "      <td>0.405664</td>\n",
       "      <td>...</td>\n",
       "      <td>2.83</td>\n",
       "      <td>40.009998</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>POLYGON ((8.685274124145508 13.63951969146729,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AREA  PERIMETER  COLUMBUS_  COLUMBUS_I  POLYID  NEIG      HOVAL  \\\n",
       "0  0.309441   2.440629          2           5       1     5  80.467003   \n",
       "1  0.259329   2.236939          3           1       2     1  44.567001   \n",
       "2  0.192468   2.187547          4           6       3     6  26.350000   \n",
       "3  0.083841   1.427635          5           2       4     2  33.200001   \n",
       "4  0.488888   2.997133          6           7       5     7  23.225000   \n",
       "\n",
       "      INC      CRIME      OPEN  \\\n",
       "0  19.531  15.725980  2.850747   \n",
       "1  21.232  18.801754  5.296720   \n",
       "2  15.956  30.626781  4.534649   \n",
       "3   4.477  32.387760  0.394427   \n",
       "4  11.252  50.731510  0.405664   \n",
       "\n",
       "                         ...                          DISCBD          X  \\\n",
       "0                        ...                            5.03  38.799999   \n",
       "1                        ...                            4.27  35.619999   \n",
       "2                        ...                            3.89  39.820000   \n",
       "3                        ...                            3.70  36.500000   \n",
       "4                        ...                            2.83  40.009998   \n",
       "\n",
       "           Y  NSA  NSB   EW   CP   THOUS  NEIGNO  \\\n",
       "0  44.070000  1.0  1.0  1.0  0.0  1000.0  1005.0   \n",
       "1  42.380001  1.0  1.0  0.0  0.0  1000.0  1001.0   \n",
       "2  41.180000  1.0  1.0  1.0  0.0  1000.0  1006.0   \n",
       "3  40.520000  1.0  1.0  0.0  0.0  1000.0  1002.0   \n",
       "4  38.000000  1.0  1.0  1.0  0.0  1000.0  1007.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((8.624129295349121 14.23698043823242,...  \n",
       "1  POLYGON ((8.252790451049805 14.23694038391113,...  \n",
       "2  POLYGON ((8.653305053710938 14.00809001922607,...  \n",
       "3  POLYGON ((8.459499359130859 13.82034969329834,...  \n",
       "4  POLYGON ((8.685274124145508 13.63951969146729,...  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We use a PySAL example shapefile\n",
    "import pysal as ps\n",
    "\n",
    "pth = ps.examples.get_path(\"columbus.shp\")\n",
    "tracts = gpd.GeoDataFrame.from_file(pth)\n",
    "print('Observations, Attributes:',tracts.shape)\n",
    "tracts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the CRIME variable\n",
    "In this example, we are taking a look at neighbourhood-level statistics for the city of Columbus, OH. We'd like to have an idea of how the crime rate variable is distributed around the city.\n",
    "\n",
    "From the [shapefile's metadata](https://github.com/pysal/pysal/blob/master/pysal/examples/columbus/columbus.html):\n",
    ">**CRIME**: residential burglaries and vehicle thefts per 1000 households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lxjt16tQ5EdE1BCEBna00uoC/ALtExLWSvgM8FRFfajZNV1dXzJ49u+159fT00N3d3e9Y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\nl6J2iYOPAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc284d1cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's take a look at how the CRIME variable is distributed with a histogram\n",
    "tracts['CRIME'].hist(bins=20)\n",
    "plt.xlabel('CRIME\\nResidential burglaries and vehicle thefts per 1000 households')\n",
    "plt.ylabel('Number of neighbourhoods')\n",
    "plt.title('Distribution of neighbourhoods by crime rate in Columbus, OH')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see what it looks like without a classification scheme:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-15T21:29:54.097280Z",
     "start_time": "2017-12-15T21:29:53.766283Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2841c2b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uWT9lYSfjmbPibbh6waj7uNHFzFGFPT9QGBb/sJMpxx7f5vsLFizg7t/9lvk/\nP5J0R3SiZoxQpMkhQXClNrKjYx4CbiaYRwggDaiXUu7+YykHOvSHKWFWdJqbb7mFlNR0Zp5yNu9/\n8DFPP/sCSVlF+Hzdy4189pk/obLFxc7m/XML+w2Dr8treH/TTt5YU86/vi/l0a838O6GHTR6ez4n\n81YJ04Z1LmIkWizeWMW0aeH9y2vXruXC2efy8vkTGJieELUx568uZ+qUo6PWX6wjdBHRQTCZ25JW\nx+V7+hBid23Upa27DjNchz95avFP0Wl0Xec/8+Zx7z338MeHniApMYnGxiYaGhpJT0/ruIM2uPPe\nB7AKjSEP/48EswlDytABfmnglZAsBGYhsABmEbQsejpfRovfoLrFzeTi3hfmJpeXtVt3ccQRR+z3\nXk1NDaeccDwPnDCcowZkRnXcZo+f/KIDc4dwQIjcY1PdTs2/ycCpQohZgA1IJGhBJwshTCGrOR/Y\nr3D1vihhVnQJXde5484797zu37+QpubmiIXZ7/dTW1tHTW0dNTW1/Oet//KvfzxFSUo8a2qauCvO\ngkmAGTAJwVUNTm6QkpFS0voe2w9cBHgNA0sPZQibX9NEYUYSyZ2IFokWX2zYwbjRI/eLX3a5XJw2\n6wROH5LKhUcMiPq4G+rcjDl+SNT7jUmilGtZSnkbcFuwSzENuElKeb4Q4jXgTIKRGRcBb3XUV0TC\nLIR4Bthtpu+ulH0PcBpBX0olcPHuiiX7XHsR8H+hl/dKKedGMqaib5GQkBBRPo13FyzktNNn45cS\nsxCYdQ2brmHXdf46pggp4f8aXQwz7x2O5zQk4VIcmQArUOb2URzXM8L5QYOT4yccmPzLi9fvYMox\nx+11TkrJ+WefSSEN3HfC+B4Z98uyWq48lPJw9Owa5y3Ay0KIe4HvaSNtcmsitZifBR4Dnmt17k9S\nyt8BCCGuJViQ9crWFwkhUgkm1i8h6FdZKoR4W0pZF+G4ij6CzWbD7fa0+b5hGGzZUsZfH/07J+al\n87ex/dHCWClbWzw0+PeuSuKRkgDBINBwJAjBJre3x4R5i4Trhh6Y+OVPN1dz17V7+5f/8Ic/8P3X\nn7Pm5hP3hMVFm9KqeoYMOUQsZoh6giIp5SKC5fR2l9bb3xfVDhHd+0kpFwO1+5xrXZY3nvAO7ZnA\nQillbUiMFwIndGaCir6B6MDkGDp0LCNGTWDjN98wOz81rCgD5MdZ8BsGFf4f/cb1hsQqRJtf1mRN\nY6u7Z9J/ug2DKqeHo4b0vn/Z6fGxfPMOJk0K7jR0Op1cftkl3H777RzVPx1LD5a2ykxyUFHRoSv0\n4EFEePQS3fIxCyHuAy4EGoAzuypdAAAgAElEQVRwy8Z5wLZWr9sMFQmtbl4O0K9fv+5MSxEjrF33\nA81NzdTW11O6vYIVM8eSaG7/K6cJwYCEOL7w+TjTFLSA62VQmNuK30oFynuoWva7Nc3kpMSTntD7\nSX2+2rSTnOwsNm/ejMfj4eKfzWZUhoXLpg6nojryOoxdYUh2Mps2bTokrGYhom8xd5durZZIKW+X\nUhYA/wauDtMk4lARVSW779N6l9gTT/6LkYcfyfHHzmL2Wefzk36ZHYrybkYlx7PCt7fFbGnnDyfV\nMNjZQwnz36tr4djDDkx0QprDRnGajXNOmcnJM4/l11MKee4X0ynOSWFXk6tHxxZAIND7hW4PGJqI\n7OglohWV8SLwDkF/cmvKCRZy3U0+Ib+L4uBi+IjhnH/BZWRkpGOxWVm1eh2GhKPTE7FpGnYM7l69\nFYdJI07XcZg0Esw6Dl0n0Wwi0ayTaDKRZNEYmWhn6a4fbYYWKan3B7jBpGOVcGMgQOuf7hQp2ebv\nmd0QmxFcMfzA+JdH98tg/rX7bywpykikKkysdzQpq22msPDQCZeLtQ2OXRZmIUSxlHJD6OWpwLow\nzd4D7hdCpIReH08onERxcHHVVb9k7tznmF61iwDBX2Cv3YKnvoUGYJcMLuK5pcQTeu6REq+U+EKP\nfhkMfwNwaIJgvAVMsZjISYqjXkoeaHazDvYS5mSgqQdimb2Gwa4WN0cfAP9yewzOTqG2pWeFeXhm\nIp9++imjRo3q0XFigsh39fUakYbLvUTQ8k0XQpQTtIxnCSGGEAyXKyMUkSGEKAGulFJeJqWsDYXV\nfRvq6m4pZe1+Ayj6PBMmTKBk+HCKt25mYjdzNdQbkjPqW/AbBiZNwyTEnvC5JF3Ha+zttkgCnP7o\nC/PCuhbSE+xkJ8dHve/uMCgrCafXT8AwupTbORKO6pfI6hXLeqTvWCTGdDkyYZZSnhfmdNhYPCnl\nEuCyVq+fAZ7p0uwUfYpLr7mG1267hYl0TySTNUG8JljpNxhr2Vt4TAL2XeZLAtxG9P2hC+qaOWZk\n/6j3211sFhNmXaPO6Y1abox90YTA7e5ZqzymiLHFP7XzTxE1zjvvPG6+4QbqbTrJ3fyiDzSb+cbr\nZ+w+1rdZCHYnsn1OCJabdLyGJGAEmLCsLLSyHEzAs9vrvOdRyr1fszvQI1QiKnRyz3Mkt6TElrUM\n8LcPVpBks5DUg5nujhmczV+f/7DH+o8pYjAqQwmzImokJSVx8qxZfLDgHc60mbvV11AN1oZxT5gR\neyzm73Sd0+MsjLGauaqygZdGFxJn0hCIUDmnULknfkwgpoUirne/L0Lx0a3fE6H3jv5iPVM6Weqq\npzEMg/ve/JY/nDIGsx59N4ZhGHxfXse7a7dTun0Hl118IevXrObm393JKaecEvXxYoYY82UoYVZE\nlV9cfTVXfvgBZ8hAt/IP5At4029wTX0LEoEUQSu2zG+wTcAyTaM+4GdmfAJ5Jp04TWDSBIPio3Nr\nX+nx4QoYTB+WH5X+osWc+Uux6oLzx3WtUvduDMNg2fY6Fq7fyTdl1WyubaGq2UOd041F1xickcSl\n4wcyqmYVPn8tny1efFALs4ixPJtKmBVRZerUqTSbzVxe14xN7HYJ/CjQe9wIe06JPa4Dn5RomsAk\nBE0BAz+SI+Lte4Lt9VBPugCdoB86N2Q1Juk6m50ehkdpI8g39S1kJsSh9dDiWlcwDIOH31vG384Y\njylCa9kwDFbtaODt1eUs317HxuoWqpvd1DrdmDWNwRmJjM1JYcbhGQzLSGB4ZtJ+pb1eWGbio9LN\nPfGRYgdlMSsOZjRN46hp03j19Te4PDlur52sez/+uIl7t+vghUYXQ1MSmJmfDsC9323ijHgbqaaO\nRSjVpLPN1W4ZtU7xXk0z44uzotZfNLj1lS/IiLfy01EF+723W4DfX7+Db8qq2djgpcrpp66xOXjn\nIgTnjsjl0rFpDM9MZFhGIpkdLBw2un08/OVG5m+qoXBEW5lKDgJ6ebt1JChhVkSdW277LV8vXMhl\nSZZOuTPmO73MHpTDeYOCft3nNuzgc7eHUxwdW8FpukaFOzrbspv8ARbuqufba0+MSn/RwOv38/Si\n1Tz/s0ms2VXP++t28HVZDRvrPVQ5/dQ2NaPrOoMHDeTwsVP4xeiRjBg+hBHDhtLQ2Mj4ycfy91PH\ndmrMlbsaeG59DU89+xxHHXVUD32yA49AIHrAX98dlDDHIN999x23/fp6dE1D13XMViv3P/gXhg4d\neqCnFhFjx46lxuOlyTCT2Imy8H4psbeqvj06PZGlO2s5xdHxtZmaYFeU8mW8s6uBnCQHw/O6nvQ/\nGnj9ft5bsY13lm3mnWVl+NA55/mvEZpg8KCBjBk9mUtGj2TE8KGMGDaErKzMsD+EDY2NYXrvmLQ4\nC1azieOOO67jxn0d5cpQdERlZSXbf1jLXRMHEjAkL6//gddefZXf/f73B3pqESGEwKRrnY5m9kuJ\nrZWQj0118NKOyPYjpQvY5O1+LLMnYPB4WTWXnDC62311Br/f4IM1W/nvd6V8u6WSirpmappcpDls\nFKUlsKuhhRuvv5obrrmS7OysTt2JSCm7dKeeEW+lqvYQyNC7O0wnhlDCHIMUFxfT6AtwwoCgj1Mi\nefrDhdBHhBkgPyubClctyZ24RfRJsLdKZTkixUGkspCiazR1U5illPx+w07McRb+77SeSUDfmkfe\nW8ZLX/5ARV0LVU1OUuxWSvpncObIPA4vSOPwglSS7RZO++cijpxQwh/vv7NL40gpu2QRBi+LLcHq\nGUTMhWUoYY5BCgsLqWxoxu0PYDPpTMpL4/IPFuP3+zGZ+sZ/2WGjRvLJR+/jC5Oq0yoEQ637xzkH\npMTWSsiHpzio8/owDKPD6IhUTXR7W/bDZdW8X93I93PO7/FojO9KK7n91S/47fGjKCkMinBamFC/\n178v5YstVWzZ+HGXx5Kya2tbLn8As8nEu+++i8vlwuVy4XQ6kVKSkpJCWloaqamppKWlkZWVhdXa\n+6W3okaM/QD1jb/yQwyTyURhbjab61sYnp5Imt1CXpKDFStWcPjhhx/o6UVEel4+jzW6eMu/vxVb\n5/Lyp/REpuwTlhWQElsriznDbsGqaaz1BRhhbV8oU3QNd4RpKt1+g7UtLlY3udnY4qHM6aHM5WWX\nz09uagJnPfI/zHqw7JXVbMJq1rGZdexmEzZL8DErOY5bTm6rJmfHXP70x1w2eQi3zRzZZpvqZjdX\nvPwVDz74AMnJyV0eq6uU1TmprK3nkT/cjt1qJs5mxm41IRDUNbupbWihtsHJjqpaxpWU8O6Chb0+\nx6igXBmKSCkuHsSmumaGpycCMCknicWLF/cZYU5OSebWo4by+6nD9ntv+txP2dDk3E+Y/RLi9qnK\nMSQ1gS9cbkaEsbBbk6LtLczzd9bzQXUj9b4AjX6Del+AJp8fZyCAR0IckKZpZApBtmHQJAT9kuO4\ndHg+3oCBJ3S4AxK3P4Db5cPd7KHRb+AOGPxty05mjixkTGHnc4d/8UMFP+yo5d3Lp7bb7pevfcOQ\nYcO47JILOj1Ga2QbBQY6osnjIy8zmXcfurzddp8s/YE75n7RpTFiAwF6z1WD6QpKmGOU4mEj2Ljs\nx1wFU7IT+eeLL3Ddddf1Cb/f8OEjeOXtl8K+l5dop6Ju/8KtAfaOygA4PC2BFZs6LvKarAs8RlCA\nnt1azZwNO5is66QYBgVSkg6kEUwXmkroi2/86PpYr2ucP7yAK8Z0vKOu3u1jxDNVTL7rVUyaFroL\nFnvuhgXBwOy2/pc8/gDXTR9BZjubYcpqm3l31Ta2bX6vw/l0RHJSIi6Pl+FPfMKMfAe/OWoohRHk\nAPFHmL1O0zQMo2fyYfcaMfY31aEwt1Eh+0/AKYAX2AT8XEpZH+baUqAJCAB+KWXX7/0OMQYPG843\nny/Y8/ong3P46/JveOH557ngwgsP4MwiY9SoUdxRFb78UX6ijW/DZIMLSLDvs5lkVKqD90s7tmbi\nhcAAntsWFOVbgNGdqMAhDEkgAstSSsk585cwICOR+VdMByGQEgwpkVJiyOBirdGBu7sorf0YwLhQ\ntZf09O6H7OXkZFO2/nv++7/3efrZFzj2xW/Y8KtwleD2xmzS8Ufwbxis+hX9tKu9hgBiaIcnRGYx\nP8v+FbIXArdJKf1CiAcIJr+/pY3rp0spq7s1y0OQ4uJiXmr8seq0SdN4eMoQzv31DQwfMYJx48Yd\nwNl1TF5eHjvrwwtzv0Q7T/kNflLdxAxdcG1KUKQMIH4fYR6e4qA+InEQ2ATc/8MOrgc6G+ymSSMi\nYfYbkq+219Dyl9lYenAhNi3eitcfwOl0EhcX1+3+MjMzuOSi8xk8aCBnn3dRRNfEm3V8ESyoakJg\ndPRLFOvEmMXc4c9EGxWy35dS7s5W/hXBghWKKFJcXMymmr03BpTkpHD3EYWccuwMzvrJqaxfv/4A\nza5jkpKScHl9uMMs/v3i8CLev+BoZgzIZFkoxM2QwdSbln0sl8FJ8TR5/TR38Ie/2esnIOEKOlkn\nPoSQQdHtiICUaEL0qCgDaJogwWZh3foNHTfuVL9a+KKbYYgzm/CH+f8L22ef9mSIoMUcydFLRGOk\nS4D/tfGeBN4XQiwNVcFWREhBQQG1LU6avXtX65g9vIBlFx7FyLrNTB4/jqOPKOGRRx6JuVLzO3fu\nJDnevleUxW7MusbhOSkUpcSjh/6i/QS/jPuGqVl1jZx4G1+2kwfDaxj8qqqJEzWN9pfT2kYDfBEI\ns9G1kOBO0+zx4Q8YBCIQxs4ghCBSZU6w6PgidGX0aYt5T67XCI6OuhLCJoT4RgixXAixWghxV+h8\nkRDiayHEBiHEK0KIdpNpd+tnXwhxO8G/qX+30WSylLJCCJEJLBRCrAtZ4OH6uhy4HKBfv37dmdZB\ngaZpDOxXwKa6FkZnJe31XrzZxI3jB3L12P58WFbFvGce4Y7f3kZeTjaTp0xh8tTpTJo0iaKiogMW\n97x69WqGZrWf+MZhMeEOCbFftm0ljEpP5Juqeo5rI6XnddXNpEnJ7G6Ig07kFnNv3PQ+9sk6cnKy\nGT8+ulE4QohQWYD9cfv8LCqtZtGmXSzdUc/WFl9En7WL+1diiKhGZXiAGVLKZiGEGfhMCPE/4NfA\nX6WULwsh/g5cCjzRVifdKcZ6EcFFwWNkG/E4UsqK0GOlEGIewbvMsMIspXwSeBKgpKSkT98YRYvB\ngwezoa5qP2HejdWkM2tgNrMGZuObPoyVVY18tWUJ85d8yv9V1LKjrpE7f/87bv/9vsXLe57ly5cz\nPLX97GUluSk8ICXrPX5qDaPNUNLDUx38Z2f4PYDPNbSw3u3lIYLi2lUiFWZdCDQhmP7w+3x83f4V\nrKNBo8vLAx+s4sV/z41635qmIQ3J19tq+GDjLr4ur6WsxUeN00Ndk5O0JAeHFecxaerhXD4ol/Ej\nOq6U7fH6+vjmEqL2yxLSwt1hRObQIYEZwOzQ+bnAnURbmIUQJxBc7JsqpXS20SYe0KSUTaHnxwN3\nd2W8Q5UhI0ex4dO3I2pr1jUOz07m8Oxkfhk6t2xXPT9/+qk2hVlKyVNPPUVSUhJFRUUUFRWRlpYW\nlXC8Re8v4Kyc8D8ouxmTnYwnEOCyHXUkWM0MSAwfqTAixcFTYea03efn6QYXNwIp+1/WKYKujI4t\n7jizzpc/m8K4uYvw+w1MEaQk7SyPLF5PdnYOJ8+KvvBX7NhBfVMTp7z0FcMG5DDu8KGcUZzHyEF5\nDB+QjSOu84UGPD5/3xZmOlVaKl0IsaTV6ydDRuWPfQmhA0uBQcDjBCPX6luty5UDee0NEkm4XLgK\n2bcRrC2/MPRH/JWU8kohRC7wlJRyFpAFzAu9bwJelFIuCDOEog2GDhvOuwve6PL1ozOTaGlq4t13\n3+W7pUsp3fgDO3dV8tY776LrOps3b+bmG65jSlEOWxtdlNY04DcMCvNyg0JdPJiiQcX079+fgoIC\n8vPzyczM7HC7smEYfPrFlzx66ZR229lMOk+cPI4r5i/l3iMGc8aA7LDtRqQ4qPfunTnujSYXD9S1\nkAN0LplleHSC7pRIGJTiwKJr7GxykR/lmoANLi8PfrCKV199Pqr97qaxqYl+2amsn3dn1Pr0ePu+\nMHciV0Z1R2G/UsoAMEYIkQzMA/bfZdWBp79DYe5khewKYFbo+WY6H7WkaMXRRx/NjdfsotI5iMy4\nzn/xhRDMLMrgrNNP5+ej+jEqyc7TH6zA7/ej6zpr1qwhy2Hn/olF9E8KCky928fWRielDY1sXbOY\nH775kA9dfrY3udhe30SDy01uRjp5OTkUFBaS338ABYWFe4Q7Pz8fv9+PzaSTFUEF5zOG5aEBl8//\njvlbq3hmyoj9hD87zooQgo1ePy2GwV+aPWwPGJw2JJfVG3dCoPsLTyaCGyoixWbS2dXkjrowP7Ro\nHXl5uZx4/LFR7Xc3eg/scAsKc89U6+4VhOiRLdlSynohxCJgIpAshDCFrOZ8oN3VerXzL4YZMGAA\nF1x8EXd//h6PTR/epT5uKynimtH9GJwadBNc/+HKPQuCkyZN4oSzzmPaC88zMiOJm8YUMLVfOsm2\nJEZlhndDuP0BKprdQaFuKmP7N+tZvTjA+y4/FU0uyuubqG1xUpyWGPEcfzosj9HZSZz3xrcMef1z\n+jvsTM9JYWZ+OskWMz7DIMli4sqaJvzAJYcP4MaJg3h++VbWbdzVpX+XfYk0KmM38WYTOxudBPcT\nRod6p5e/fLSKeW+E3zEZDTShdXmLdlscDK6MaC3+CSEyAF9IlO3AscADwMfAmcDLwEXAW+31o4Q5\nxrnj7nsZM+IN/rVyKz8f2floldxW236llCTF2diyZQuDBg0iPT2dhx59jDl/epBnn32W6+/6Hd+f\nn95ufzaTzoDkeAYkt20p/lDbxKTnPsFvGJgijP0ckOLgi0umsaSilkVl1by7cRfP/LAdX8gaHpSW\nwOzDCrhwdCFJoQrcXiPQrQW/1uh0TpgdFhNVzZ6OG3aCvy5aS79+BRx3zHTcbjdv/3cBCxZ+xC03\nXsOQwcVRGUPXdYxoC3Nft5ghmmElOcDckJ9ZA16VUv5XCLEGeFkIcS/wPW14HXajhDnGSU5O5oNP\nPmXa5EnEm3XOHtrumkG7CCH4zfhBnDLzeF5/ez7Dhg3jheef5+knHueYE2dRVlNPpdPTJbdJawan\nJhBnMbGqspEx2ZFnRdM1wYT8NCbkp3HL5CEdtvcFZFSFuTOujGSbhZ2NrqiMLaXks02VPPjBSvJz\n88jOLKSmuYU0swkhJS6Xi5ee+2dUxuqJzSCGlGhh4tX7DBHGKEeClHIFYZY9Qq7diPc+KWHuAwwa\nNIj3PlrEsVOPRhdwxpCui/N144pIs21j2uRJZGdlEedp4ZeH5bL47X9jM+lsqmvutjAD5DrsfL61\nulPC3FkC0qAsEOBfmkaGYXA00H4cSNsEfcyRK1aqvevCXOf08Mp3pfxvdTlrttezq9GJAEbHWTnC\n1cDIZCsjchJI1DVerWvhP0u/79I44dB1PequjIOCPpgrQxEDjBgxgncXfsgZp5zMe9vqmDN5MKn2\ndjcPtcnPRhQwLjuZrQ1Oji8ahhCCM4fm8dC0YWhRshxmD8/jL19t4BfjBmDpoUKX104YhNMXYHNt\nMy9triJdSiZ2sS8TnXNlpNstVDW7O2xnGAYfb9jJWyu28eXmKrZWN9Hg9VFgtTA+3srlcWZGp2WQ\nZ9bDhimOsJl5fHv0dnVqmoi6K+OgIMZ2yChh7kOMHTuWFevW89ubf8OEF1/kz1OGcGpxTpf6GpaW\nwLC0hL3ORUuUAa4rGcTj35fy5HdbuHr8wKj125r0OBsPHh8M/Mme8xZZga4Ljg54OuHKyLCbWdGw\nfzrSlRV1vPZdKYs37mJLZRPVLW7smmBkvI1pNhOjshMZbjNjj9BCK7aaafJ6qa6uiUqmOV03KYt5\nX1SifEV3cTgcPPK3Jzh79vlcesH5vLG5igePHkJGFNwP0ebP00Zw6f++Q0q45oieEWcIWqUtAYPO\np6z/kc64MmrdXnY0u1i9s57T//kx2+taKK9pptnrJyAlw+JsjLOZODvZzmE5iaR3w/9q0QT9bBbe\nmDefK35xcZf72Y2mCSXM+6ES5SuixFFHHcWyNeu44/9uZ+LTT3H3pIGcN7wgqlZvdzmlOIe34iZy\n5pvfsLm+hXunDSfe0jNfuUy7lYc9Pm41jC4tCIbbkl3v9vLR1mo+L69hZVUjFY1uat0eXAGDTF2j\nwKSjbaykBMn3Li8vFaUz1GqOeiGDMXYL73/4cVSEWWiij2eC6yFi6O8GlDD3aex2O3/88184+7zZ\n/OoXl/HU2iX8cfIgxud0d4Ny9JiUl8Zn50/hJ/O+4eWVW3nwuFHMHlkQVfHSNI3lVx9P4YP/ZTvQ\n2aBCF8Fo/3VVDZzw6hdsb3RR6/LgDBik6xoDzCaGCDhB1yhy2MjXBKZ95v+ax0+930DYuve5Nrh9\n3LOrkanxFgbbzBRbzYy06Lz8/fJu9bsbUw8s/vV5C1wItfiniD4lJSV8ufQ7nn/uOc6/+SaOy0/l\n0elDY8Z6LkqOZ/nPp/Pi6m3c8sEqnvq+lMdnjWFYekLUBNphMRFv1mnaJ01qa5zAcmANUArU6TpN\nhoFTSgwgweNlYF0zx2qCongrBbqGOcL55Zp0Vrm8TIpgt2O7n0MXLHN6qE1Oxd/kpG57PT7DIN7W\n8UJjJGiaFvXFP6fLS1xc7xeLjSox8reyGyXMBwmapnHRxRdz+hlnkJOVyb1HDiTF1rWojZ5i9ogC\nTh+SwyXvfs/R/1qETdeYUJDOtMI0xuemUuf2sqaqkQ11TmYUpnPWiPxO/bjYzSaavX6agRXAaqAM\nqA8JsEtKUoWgUNMYEQhQGAiQD1iA64F/JMSR18WkRBm6Rrmv+7mTc8wmrs5M5LWWZsrLN2AymVi1\nag3VtbUdXxwBwXC5qHS1hyanm8RUJczRRAnzQUZCQgKCYHrKWMRmMvHiqeODiY7Ka/jP+gpeXFnO\nX77ciNWkkxVnJddh5aaFK3lz/U6e+8k4zG2E2+1sdvPBpl18WV7L6soGqlvcPEwwQXiqEPTXNEYF\nAvQLBCgguCXLLCXsk/z9TSDfZOqyKAPYAVeUFO/S1Hg+3FrLmedcxJtv/JvDDuvadvxwBDeYRFeZ\nm5xe0vtHvgU/5uijNf8UfYyAYaDHWPjPvmiaxtR+GUztFz6Wotbl5YjnP+HG91dwRF4KK3c18kNN\nI9sa3FQ3u2nw+PBJSYqmkSUEmYEAuUAlMAcoCiPAbVGjaWRHXHApPHagJkqVonUh+HNOEmcseJ95\nb73DT087KSr9Auha9H3MTS4vRQkJHTeMWaK38y9aKGGOEaSU7Nixg02bNu05aqorcbY0c+HFlzJj\nxoxO9Ca4ZtF6+sXp5MZbyXPYyU2wkeuwkRFnjRnfc3uk2i3MP2MiE579mLnfbaFI10mVkkLDYAyQ\nTnCXn9Yq9tgNvAE8A9zTibGahcDRzR8yGxJXFKsr5VtM3JyVxCUXX84xZWtJTIyORdoTPuYmp5eE\nPi3M9D1hFkI8Q7BSSaWU8rDQuT8BpwBegkmgfy6lrA9z7QnAwwSjkZ6SUs6J4tz7PJs3b+bJf/yd\nr774jBWrVqNrGsX9cxiQn86A3BSGpThYV7+Tvz32cKeE+aNFi1i3bh3btm1jXelmPtxaRvn67Wzf\nuZPGZic5KYnkJsZx6ZAszh3W9e3dPc2wtATGZqVgraznuAisXxuQpmkEOlliqgXI7+bfpRXwRlnw\nTk+y80GzhxNmncEXny2MSp89kSujscUTtR+OA0ZfE2bgWeAx4LlW5xYCt0kp/UKIBwgmzr+l9UWh\n7EqPA8cRzNj/rRDibSnlmmhMvK9iGAYLFizg8Ucf4ptvvuWi0ybx2wuPZGTxWWSl75/poaKyjtFn\n3kMgEIg4l+7EiROZODH85mS3201FRQXz5s3jtacfj2lhBrh1UjEXvPkNxxBZ5WCXEDiBeiDS5agW\nIKmbf5gBKaPu1xdCcG92IqcuW85jTzzF1Vdd1u0+dV2nK8pc29DM8g3bWbNpBz9sraRsRw07a5po\naHGzq6aRc5v33wXZt+hjwiylXCyE6L/PufdbvfyKYJ7RfTkC2BjKqoQQ4mXgNILRSocctbW1PPPM\nMzzx+KOkOCxcdfbRvHrP6dg7iJzIzUwhKy2JZcuWMW7cuG7Pw2azMWDAAE466SSe+FPs38AcU5iJ\nH2gkMqE9LBDgU03jUsMgT9d5JAJL2yklCd0UVQ9QEwjwWm0LASQBghW1A4CBxJDgl3LPOYnEL8Eb\nOu+TEp8En5T4CT73y2CbBAE33nQbp592Erm5XduCv5t9c2V4vX7WbNnByo0VrC/dxebtVWyvaqCu\n0UWT002Ly0OLy4PfHyA1KZ6cjGTys1Lpn5vBUYcXk5uZwt9f/+yAFf2NGrGly1HxMV8CvBLmfB6w\nrdXrcmBCW50crFWyV61axV/+/CfmzZvHSVNG88J9F3DEyKJOxe/OOGIIH3zwQVSEeTcDBgygvLYB\nb8DosSRD0eDhJRtJ0TSSI3RPFAPFhoEL+FMggBOI6+Aat5QkddPHXGpItngDvBGQaEKgIdBEMP9I\n8Ag+1zWxp6CrJsCiaZg1DbMuiNc0LJrAomuYNRF6L9jmzU07ueOeB/jnEw91a56C4F1b7sxbaXF5\ncLq9OOJsZKUmkp+VQr/cNI6ZkEtuRjJ5mcEjNyOZtOT4Nr+z364uo6IieomWep2DbYOJEOJ2gtFJ\n/w73dphzbd5DHWxVsrdt28btt93Ce+8t4LrzZ7D2rbvJ7ERVj9YcM2EIT7z1P2655ZaOG0eIxWIh\nPyuD0gbnnuomsci/VpRR0kmfMQSjJBxCsEZK2i3QRnDRMLmbFrM0JNeMGcC9Rw7tVj9tUZwcz+/+\n+w50U5hNJhNNTg8LnoCnuu0AACAASURBVLie3IxkctKTMJu7lyciLyORbVvLutXHAacP+pjDIoS4\niOCi4DEyfPxNOVDQ6nWHda4OBpqbm5kz536e+NvfuPKsKfzw33tIiLd3fGE7TC0ZwoW/fQa3243N\nFr1KEcWDBrGxrjlmhXlLfQsVTS7O7eL1aZrGD4FAh8LsiYLFnCAEtZ62dx12l1lFWfzq45UsW76S\nMaNHdrkfk9mMI87KxFEDoja3/KxUnn56PukZmfTr1w+n00lBQQFjx44lOzt8gd3Y4yAQ5lC0xS3A\nVCmls41m3wLFQogiYDtwLjC7S7PsI7z33ntcdsnFTBk3kKWv3E6/nOjUg0tOjGNEcQFffvkl06dP\nj0qfAAOHDmPj2k+j1l+0+fWHKynWdewRxiPvS4aURGLHeYGUbgpzohbMOtdT2E06Jw/M4Y675/DW\nG+FuUCND64EkRqdMHYXH62fV+k9Y8VkTcTYzT/6wHVdAZ9mKVZjN5ugO2BPEli5HFC73EjANSBdC\nlAN38P/tnXd4VFXawH9nSjKZ9A6EUAMkoQSko4IIShMQUSlKWUVYFnBVRNFdpImCothwPzvogtiQ\nskoVkCZKV6RIlZKEFBJSJtPP98cMLCVlZjKTDOz9Pc997tw7557zzmTy3nPf8xaHF0YgsM5pd9oh\npfyrEKIWDre43k6PjfHAGhzuch9LKX/30eeoVoqLi5n09ERWLl/Kh9OGcVenpl4f4862jVi/fp1X\nFLPRaOSlmTNZsngRH93VzAvSeZ+Xtx9m86ksHqtEH9F2O4dVKijHFGLFsRgXVYlxACKFIN1kqWQv\n5fNAwxo8+fMvlerDF5F/wUGBjOjX8apzUkp6jn+Xd955myeffMqr4/mEG82UIaUcUsrpUgsJSinT\ngd5XHH8PfO+xdDcAO3bsYPjDQ2mXmsC+r6YQGe7dcvaX6NYhhX/8azWzZr3kUnuj0Uh2djbZ2dlk\nZWWRmJhI06ZNkVLStmUaNWzFbBvUkdphlTOz+IKFv/3Jqz/9wTAqV4M6Coc3R3kYAC0OhVUZolQq\n8l2YMdvtdsx2O0UWOyVWKwarDb1GTWJoRUuUkBQRTGFRcaXk1Kg1lYxxdA0hBG8+PZDOo2YyZMhQ\n/zZpCG48xaxQOmazmenTpvLRh+/z1uRB3H93RZbMyoxlRa0S7Nn/G4sWLcJisXDx4kXy8/PJz7vA\nxfwL5GRlOZRwdjbZuXkYTSZiI8OJiwrj4PEzjBs3ntfnzUMIwbCRI/nojdfQqv3rxwiw4mg6T6zd\nz/04FiUqQxRgqGDhsBhcziBXHmECzhQaaLjgB2xSYrdLx9652exOVzkpHblMVAKNSqBRqTDb7IQE\naKgbpmdQUk3GNK9b6o2idqiOIrMZs9lMQIBnCap8MWMui+T6NfhLvw488/RTfPrvxVUypmcIEDeR\nV8b/KgcOHGDYQ0NIiApgz5f/pEYpgSHukJtXyK9Hz/HLbyc4fvo8f2Zc4HxOAfkFBgqKSygyGAnV\n62icGMuS914lIiSIcH0AEcFaagXrSI7VEduoBrERScRFBhMbHkJ4iA4hBN/8+BuTP97E9BkzLo/3\nzOTnMBqN9H3/Xb67t3WVVD8xWW3sSM+jaUwoMWWM9+/fTzNh9T76AY29MGYEDv/i8lzmvKWYIwQU\nW2ysf7izw9VNrSJArUKjUv3X/U3tcI27No+J1W5nb2Y+G//M4d39p5iz+zjdE2OY3rEJCSH/faIJ\nVKsRCAwGg8eKWa2p2mKs/xzVizYPzeHLL7/kwQcfrLJx3UaZMd+42Gw2Xn/9NV6Z/TIv/f1eHhlw\nW5m+nXa7ncyci/x+LJ3DJzM4fiaLP9NzOZ9bQH6RkWKDCYPTed9msxEZHoLVZkOrEgzr0Zr6HZpQ\nv2YU9WtGUjc+El2g+wsombmFjH9zJSu+W31dLoMpU6dhLCmh36IFfNe/tceFXcvjRH4x605msT6j\nkK2nMgkJCWFwg2hm3tbkurbzfjnKzC2HGIBjprsPhxlC49y0zr3AoWzNzn2WSoXBbicAR/rOQByh\n2Zdea4GVQBoORR3pPH8JbylmvUpFaICGFnHu36Q1KhVta0XRtlYUkzo0YtOfOby56zgtF/1IXLCO\n7gnRzLw1mbAALSoBZrPntmxfLP6VR2iwjsUvjaTP3/5KmzZtaNDAe94gXsVLelkIkYgjSroGYAfe\nl1K+KYSIwhHvUQ9HOvAHpZR5ZfWjKGYX2bBhA+PHjeWPo8fo0qYxX6z6hQ+/2UKJ0YzBZMFktmI2\nWzCZLZjNVoxmC1qNmsjwEGrERpAQH03dhBp0aN2UmnFR1IyNpGZcJLXiIokMD3GE377zFWs3/Mwr\nY72TTex4ei5169Shffvr43qEEMyaPQeTycS9S79gZf9bCPdA+V/Lifxi5u8/zfozFzDYJD169GTk\nmP78u3t3Tpw4wUN9ezHztquvGbTsF747ngnACq2WmrGxtGzVivbNm2MsLqa4qIjioiIMRUXY7XYi\nIyMJd26zXnqJ8Wn1sQGFZisFFhuFZislVht5Fht1LRY2W+2stjjOmWx2VEKgVQm0KhUCidVq58nC\nEtpoVNwdqCXWA3tzMMLlmoHlIYSga71YutaLpcBkYf3JLP615ySNFmwgJToMq11itniumNUqNeWE\nE/iE1ql1ef6Ruxn84EC2bv/Z49m+T/HejNkKTJRS7hFChAK7hRDrgJHAD1LK2UKIycBkrkljcSWK\nYnaBjIwMBtzbD61axT13tiUmMpSYyFCiwkMIDwsmIiyYiFDnPkxPRGgw4aF6AgLcU3RhIUGUlFOB\nw10S48I5l5FR5vtCCObOe4MJJiMDVq5geb9bCK1kTb71p7L4TR3JN2u+okWLFlc9UURGRlJshyO5\nhUTotKw8lsmyP/M5mGdixIgRjB49mhYtWhAS4rpf9cqvv+L+pFhaxro2U5VSUmK1U2SxUmSx8vLO\no6w4nknTkCA2G818km9Ar1YTqxY0AboGaGitUVW4OBisAosHgTDlERao5b7kBO5LTuD0RQNPrf8N\nXa4ak9HkcZ++SJTvChOGdGXDrmM8N/lZXnt9XtULUCHeUcxSygwgw/m6UAhxCEcUdH8c3m0AC4FN\nKIrZc4qKirind08iQgKpWzuepe8+47OxQoKDMHqhCsYlakWHIW1Wvv/+e3r37l1qGyEEb83/F2Me\nNXH/f9axakDrSqUFbRgRTIDBTlpaWqlj9e3XnwFLv+KiyUqvnj14fM4wevbsSVCQZ94hKampHMk7\n6bJiFkKg16rRa9XEEUisPpB2IUE8GR0MBGORkj9MVvYbLewy23ix2EiJXRKt1ZAg7bTQqAiQkjN2\nyLRLCrQaioWgxG5H48Uc2DvTL3Aq34DBYqXYYqPYYiM5OoT1JzO5s1d/YmNiEM7QbnB6lVw6FgIh\nBEKocBwKVCoVQghsNhtWq42uo+Zitdmx2yRWuyQxPoJ/z3oEnY+q3ggh+OiFobR5aA53dL2Tvn37\n+mQcj/BRSLYzx1Ar4Gcg3qm0kVJmCCHiyrtWUczlYLVaGfTAQFrWjyDt7hQW/GenT8cLC9Fj8qJi\n1mjUfDltCP0fHsq59MwyowZVKhUzX55NowZfYLbZ0Wk8D9FNigzh2JayC4c+N+UF+vS/l+7du3sl\nijG1ZSuOrPQ8L5YKhyHwElohaKrT0lSndUZDhZJltfGr0cI+k40lF4sJ0wfSrnYMt4cHUScsiNph\nQSSEBlEvwnuukkP/s49b2rYjJjaW4JBQgkPDiAgNpalpKadPHufvQ7sicXh/SCmREuzS7thfPufw\nCJFOLxG7c98xpS8BWg1ajRqtRo1Go2bFhj0k9nyO6IgQVEIgVMKp+B2/D61GhVatJkCrJjBAg81m\nw26XFBstlBgtGM2OzWS2ktqgJhs/nHjdZ4qOCGHRrJEMfHQku3bvIzEx8foPXl24fk+NEULsuuL4\nfWc6iau7EyIER3rwJ6SUBe7WtlQUcxlkZWUx/OGhCGMu784YzVfrd1Nc4p2CmNdit9s5nZ7DidOZ\nmC3eDevt1KweZqeLVXmKcPGiRfRvklAppQxQOzSInPx8DAYDev31vhB16tTxapKqps2a8ckSz22u\nQghkBTbXOI2a7iFquofAIbOVPmn1eO423+TEuIRdwnufLKRWrVpXnU9JSeGz91/jiZG9vDrekyN7\nsXbbb+RdLMZmtzvc/Zx7q82GyWTBaLJQYjJjKDHz6kf/4YmHu5MYH0lYaBARoXoinL7Yfce/TUGR\ngbCQ6//+nVo25MmhdzD4wYFs2rzNf6ICXVecOVLKcn1jhRBaHEp5kZRyqfP0eSFETedsuSaOYjtl\noijmUvjhhx8Y/vBQRtzTlmljRqPRqIkK01Pio5Dbp15awAdL1hETGUrnFvW83r/dLivM5bzgg/eY\n3SK+0mOpVYK60REcO3aMFi1aVLq/ikhNTeXIhUKPr1eJq2fMFWEDNFWQjU8ISnVra926NU/+fsIH\n4wl63Oba3ysr9yLzFq5i7sQHSvVKSmlYi9c+Xc/0v/Ur9fqnh9/Fj3ve4x/PP8crr86tlNzewztm\nKOH4Qj4CDkkpX7/irRXACByVz0YAy8vrR1HMV2C1Wpn6whQWfPwBn0wbRvcOKZffi44IwWTyjWIO\nDNDQuUVdVs2tTABy2dhstusWrzIyMti+fTvbt21l86YNnDpzmtv7eD4LPF9s5MvD6Sw+lk0Jaq8m\nWyqPpKQkzuYXYLTaPJrtq4R77mN2Kb1qSy4LgShVMdevX5+AAB3b9/5Bp1be8PZ2H7PFEfBU1uP5\nYwNv46lXvuRfX/2IRq1Go1ERoNVwd8cU5j75APqgABZOH0brh2bT5Y6u9OnjvZqGHuM9r4xbgWHA\nb0KIfc5zz+NQyF8KIR4FTgMPlNeJopidnDlzhqGDHyBIlLBr0WTir0nRGR0ejLES/qPlUTM2ko0F\nvjGTgEOZ7N27l127drFj22a2b/+JwqJCOjRrQMfkmrw8vAODpx5j1Ynz9GnoeuhsicXGd8czWXws\nh1/O5dC/Xz/emjqazp07VzrE2VW0Wi31aydw7GIxzTxIq6pRCaxuaGa7hKoImCxrxiyE4OlnnmX2\nhwtZMb96FHOtuEgsVhtmi5UA7fUqZPTAztzVIdWZZN+MwWjmfG4Bb3++gZrdn6ZNs4YEBgaClPx1\nzGjOnD1XDZ/iCoT3irFKKbdS9vS7m6v9KIoZWL58OaNHPcKTD3Xl6eHdS1UqUeHBGH2UpCYuOoJC\no+8S4ESE6Bk9YjAdUxPplpLAlL5DaZwYe9WMZ3D3Vry7+48KFbOUkp/OXWDxH+dZfjSd1rfcwoh/\nvsjSAQMIDvZNnpCKSElJ4UheukeKOSk8mCVuLLjakGirorCAKH3GDPDII4/y4swZ/HrkNC2aVH1R\nCZVKhS5Ay4WLxaVGvQohaFD7+urnQ3q14/m3lnI4J4DRY8YQGRlJQoKflDZTIv/8B5PJxKSnJ7Li\n269ZOncUHdMaltk2PCQIq9WGwWBEr/fuY3p8TDjFHtivrVYrX278le93HObDZ+5HF1i6q1PGt/+o\nsGLKoDvT+HzdnjLfP5lfzOJD51hyNIugsHCGjxrNi8OG+cU/VmrLVhxec8yja9vERZDtxoKrXYKm\nCv6JL1UaKQ2dTscTTz7F1HeWsvStCW5Vw/EWgQFa8goMbqcjiAzVUyswtkz3zWrDzxRzhbd+IcTH\nQogsIcSBK849IIT4XQhhF0KUuUIphDglhPhNCLHvGheTaufo0aN0bN+GM4d+ZvfiZ8tVyuD0fw0K\n4OTZchdTPSI+JgKDi4r5zPk8np6/kuYj5xF5z3Se+3AtWw6cZvwbZa8luPKP27FpHRCC1c4IPIB8\no4VPfv2Tu7/dw53f7KIorStfrVrLgaPHeXbyZL9QygBNmzXnqMEzb5b6YUFYpOTLfAM/Fhs5YDST\nYbFiLkMp2pBoq8BMI8qZMQOMHz+B9AtmZsxf5nNZSkWAxeq+a+f9d7fhiyVLMJt9l7vaIy6ZMyra\nqghPq2QfAO4D3nPh+q5Syhz3RfMdX3/9NWPHPMa0Mb356wOdXZ5xRITqOXk2i6aNvfv4WCMmgpIy\nornsdjsrtx/kw5W/sO/EeXLyCmmflsSYId3peXsaSXXj2br7CH3GzOX1cX0JC/FsNq9SqRh0Z0ve\n3nsUIQSfH8tm3YlMunW9g2fnzaBXr17+49p0DampqRzO86xK86XAjE/NVlQWQZHZSonFislqdyYe\nUqNVO8K3tSoVBgEzdxzlnf2nUQvQCFALgVY4fKAD1CoC1Sp0WhWBKjU6rYogjRqdRoVOo0av1TgC\nXDRq9AEa9Bo1wQFqQrSay/vQAA04/Y/LQq/X85/vV9OxQzsSa0bxyMAunn59bnM2M5dig5Em9dz3\n4qmfEIO02yksLCQ62juFJLyCn82YPa2SfQhcm4n5E1arlecmP8vXXyzi+7f/RuvUum5dHxkezOn0\nbK/LFRURgtlipaDISFiIjqy8IuYv3caK7Yc5mZFLYICW/t3aMGZ4b+5sn4o+6OrsbLe1bkKb5g0Y\nPfcblkx7yGM5erZvzKK1u3nlZAnDxzzFe0OGEBVV2RTyvqdx48acys3HYrO7bf8tcC7oHp3Y+yo3\nOLtdUmyxUmCyUmiyUOjcD1i0nUfuaknTenGYzDYsVhsmi9W52TBarJSYHK9LzFYKzBayzY7zRoMJ\ns8XgaGu2YnYuoJmtNixWOxarDYvNjtVmQ0rIzc0tN+lPfHw8q1avpUvn26gVF0HP26+PtvQF67Yf\noF6tWALdTDlwCV1gACUlJV6WqjII/K2Eia9tzBJYK4SQwHulRchUFefPn2fwg/cTYC/kl8+eITrC\n/Tp3MRGhpGeVmRDKYy7kF6ELDGDAPxZwPDOf87kFpCXX5aF7b6d355akNKxV4U3w1UlDuGPYi+Tk\nFxHjwWcD+GLT74wd/3dmz5nj0fXVhU6no3Z8PCcKDDSJdO+z7z5/kejgwOt8k1UqQWigltBALY7S\nrg6CdQE83C2NtIY1vSF6mfSb/hVnz56lbdu25bZr0qQJ3yxdxr3972Hth5NIS3ZvsuEJugAtdulZ\nXpCDx9MpMZkwmTzP9+F1/gcT5d8qpUx3xoWvE0IcllJuLq2hEGI0MBrwamQYwE8//cSD99/HyHva\n8sLoh1B7uKoeFxlKhpuK2W63c+TEOXb9dpwDf5zmj1PpnMu8QH5hMYVFJRQWl2CxWImJCiMhsSZ/\nG3kP3Ts2JdyFihZXcktqPbp2aMpfZn/Fytl/cetagI17jrFu1zGOf/WD29f6A6kpyRzOy3VbMe/L\nvkhdN66RUqKuAhtzs8RIfv31VwYMGFBh21tvvZW33/kX/cf9nW2L/0lCvG+fckL0OoweJtt68aM1\njJ/wBA0blr+mU7UIEJWLePU2PlXMzlJTSCmzhBDfAu2AUhWzczb9PkCbNm28kv9KSsm7777L9Kn/\n5IMpQ+nbpXKPerFRIZzMuHj52Gq1kpNXSGZ2Pjl5BeRdLCa/oAi1Rs2hY+fYd+g023YdQK0S1IqP\nol5CLEl14rm9VRKJNaOpWyuGOjWjiYsO84rf7+TH7uHece5n7tq45xiDZ37BF18tvS5v841CXGId\nPly1l4smC6lRITSLDkWncfy8T14sptd3u0FcqhyiQoXDNlxgMpMc6/pnlrJqIv+a1Ytl+b7dLrcf\nPHgwJ0+eoO/YN9iw4FkiwnznuhgaEoTZQ8Vss0uaN/e8yrfP+F+ZMQshggGVM/VdMHA3MKOCy7yG\nwWDgr6NHsX/XT2z9eCJJdcpN5nQVF/KL2LbvOLsPn+bgsXP8mZnPhYJi8i4WYzJb0Dcd7MjSZbMT\noNUQFKglSBeIVqvmTEYuXe+4ne539eSJ/o+yoV8/rL9/WiX2+OaNEskvNGA2WwlwIX3noVPnefHf\nP7Jh7wm++Gopd955p89l9DY7duxgzqyZbNmyhbS60bx7LIPM/CIKDGaCAjQEB2qxWG10bVGPJ+9p\nTYnZitFp37XY7Gw/fI7Vu10PcbZL6fETlzs0rx/PrG9WuXXN5MnPkZmZQY/H5rLmg6d9ppxD9YFe\nz+lS7dxopaXKqJJ9AXgbiAW+E0Lsk1L2uLJKNhAPfOtUSBpgsZRytW8+xtUcP36c++7tR/N6EWz7\nZCJ6N6tz1LrrGWKjwmiQGEeT+rVo17IJ9RPjqF87ltjIUIKDAtEFatEFaq+a6b7+ySrW7zvPqjXr\nEEJgsVhQq9VVtkgaEqwjIlTPtgOn6HpLUpntTmVcYNrCjazZeZQnJz7NB0snuJUD2R/YvHkzU557\nhj9PHOep3ml8+s4jBF+RstJqs5OZX8y5C4Vk5BXTtWkdwoOvL2lVLzacL7YddnlcKSWaKjBlNKkd\nw6nTZzEajS6HtwsheOONt3jiicfp8dhcVr//tE+KA2s1GuzVkdTZp9xgM+YyqmQDfFtK28tVsqWU\nJ3BU9KlSVq5cyaOPjOCFUT0Z+2AXt5Wi2WzFarVxZtNbbo/9+qdrWLt+0+UxHXlwBdv2/MGtt1RN\n+GxKwwQ27T1eqmL+40w2by/dwZIN+xk/4XGOLllLWJj70XL+wKxpU7i9poq1E4ajLSVHhkatonZ0\nKLWjyzdTJNWI4KLBhN1ud8mc5Jgx+/6fOECroWHteA4dOkSrVq1cvu6Scn564lPc9tCLLJ//d5Lq\n+nGFar+gan2UXcG/5u+VwGazMeWf/+BvYx5l6auP8bdBd3g8Uw3SBbBl1xG3r8svKKJ+/fqXjzUa\nDZ9++hkPPDnfIzk8IS25LruP/Df3gNVq49vNB7h70gK6PPERYY06cujIUabPmHnDKmWARsmpRAYH\nlqqU3SEqNAi1WsXRXNf8oKWkShb/AJrVi+PAgQMVN7wGIQSvvT6PxydO5vaHZ/kkKOqmQuBY/HNl\nqyJuCsWcm5tLn1492LJ2Gb989gydWnq+4hsQoGHc4K488g/3PPvsdjtG4/U5jwcNGkSxwUh+QbHH\nMrlDs0YJnMjMY/nW3xn3xgoaDJ3LvO8O8ZfHn+f02XRenj2HuDjX7e3+Smqz5hzO9Dzd55XUjQ1j\nyynXYqDsUlbJ4h9As8QIft2/r+KGZTB27N/o3eceVm8uu3CBgpMbMPLPr9m3bx8D+vclPEgwcfhd\nfL/1NzqlNaRJPc8f38Y9eAdvLd5Q5vs7fzvBjn1HCQjQEqDVEBigITevkMZJDa7Le2yxWIiICOfP\n9ByfrpRfIrVhAucvFPLuuhP06NOftS++R2pqqs/HrWpSUlL4/P/yvdJXcu0Y9p7Lw97ajtFqx+CM\n/DNa7Y4CrlYbJRZHIVe73Y66CtJ+AjSrH897W/ZWqo9Ot97Oj98tYuxQLwlVSXLyirBUopisb/jf\nCzDxOevWraVunUSCg4NZuTuX4yf/5MCxDF6beL/HfUZHhGCxWJFSlmoOmffpOvLMOurUScRiLsZk\ndDjMPz3p6tqKVquVIYMeoHVKIk2TanssjzskN6iJHRVrN/x4w0VmukNKSgqHTnvnEb3EaOaj/Sf5\ncNcJVM51AbVKoFapHJva4WKnVqswWWzYbN4tuloWzevV4MD/Vc6vvEuXLrw4fUqZv+WqoKDIwKsL\n1/LZip84cz6Ph0b5U9SfkxvNK8PfmTTpGSZN+m+B1Dlz5pB7eFOl+gzQqpCAxWIr1e3MYLIwdtxE\n7r333nL7ycnJ4fvVa9j99YwqcbECCA4KxGgyYbFY/LNMvJeIj4/HapdkXzQQG+5eMM61aNQqxvZt\nz1tje5WrvMwWK0F9Z/L4/O/IKzKQfbEEhKDE7AixvhyabbYyrl875jzWs8KxS0wWzFaH66XFub+0\nma02snMvkJeXR2RkpEefrVGjRpitdo6fPl9li4AFRQZWbf2dDb8cZsvuPziVnkvTurFMeaATC388\nTN26vo9OdBs/m8Tc8Ir5SqSU7Px5B60SPE/LaTZbaT30JW5JrYdGU7oyzS8oJjy84nSHNWrUYMaM\nGbS+/wUsFivhoXp+/mIaDRJ9Z+Pdf/g0qcmNb2qlbLfbmT//HQQSswcZzq4lPDiQi8WmCmeUKpXg\n0V5tQML6vccpNlqY98S9RIbqiQjROfdBzP73Bv44m1vhuCcz8kh+9E30QTo0ajVardZZ8UONRqNB\no9aQ0qQxJSUlHitmIQR//etYBkx4m7nPDKJ7x2ZenSRYrVbW/nSQZRv2cfB4On+m55KdV0jN6FBa\nNqjB+J4tubdDE2o5vWM+3fyHI0m+36EoZp+xYsUK1q9fxwffzSq33cIV21mwYjt2O5cLTl6qIJye\nnU/92nGsen9Sme5Tmdn5LmdauzSjX7JkCc9MfJy4KN96Quz+/SRt2rbz6RjVzcsvzWLpp++zdcYg\nEipwh3OFyGAdp/IqfrzWqNW8//e+AJzNzqdZ40TGDrj1unax4cFkZV2osL/cQgMtUpPZvd99zwt3\nmDptOg0aJjFl3lzGTF3AoF7taN20HmnJdagZG4FGrUatVqHVqK/6zUspKSwuYeevJ9h54AR/nMrk\nQn4h6Vn5mExm6nSfRM7FIiKCdXRISaRns9q0va8dt6YkElJG7IDVbve/LIVCIFT/QyHZVU2PHj3o\n2LEjo2ctZvSATmg1alo0rn25eu8lFq/6hYvFJu67q63ThqhCo3bsg3QBDO9/W5lJ5wGeH92HoUMG\n8fMvu6hZ07VkNmFhoSTWqUetLo/TKrUBcyc9SNvmZWcO85RdB8/Qqcdgr/frL+zatYs3X3+NnS8N\nJjHGOze56BAd+8+6lwNFq1FhKmO2HhykZevvZ0ga+SaOdUKBSuWo4+dY3BcIwGy1odFVTWDPsGHD\nGDZsGPv372fZsmV8+eMunn9zOdk5udjsdqxWKyqViuSGieh1gZzJyCH7gmNx1W610rJhTRrUiCQx\nNIi0ZgkM69SI5NrRpNWPJy7C9UVtq82ORuOPakeZMfsMnU7Ht8tX8viEcby8eCclJSWcz0xn+bwx\nNG343zLwfW5rhjCSwgAAD+tJREFUzgfLtvPPseXbiMtieP/b2PDzYZYuXcq4ceNcuqZ37z707t2H\nzMxMGjVK8pnL1a4DJ3n8H+VWV7+heXnmNKYObOs1pQwQHRpEgcG9motajRpzGSWpxt53K7c0SXQ+\nhdmx2SU2mx0Jl4/tdsnRM9ms2lu19e7S0tJISys97stgMHDw4EEMBgN169YlNjaWAwcOMP4vg9kx\n2ztuHeXlmK4+hLL452t0Oh3vf/DR5eNPP11ItzFPsmjWCLq1d1S9HtyrLZPmfU2J0UyQzjNbbPsW\n9di7x/2iLO++O5/+d7amVWo9j8Ytj+Onz5N1ocA/k8R4CbvNRrwbMzRXiAvXU2hwLw1lgEaN2Vp6\nvoi4yFD63ta0wj52Hz7Dmv0Zbo3rS/R6PW3aXH1TDwkJoajEe9VGQnQBFBV5VtTAt/jXjNm/bhM+\nYPjwEcx48SVeW/Tj5XNxUWHUTYjlmblLPO43PjqcnCz33LUyMjKY/87bzJxQcSpHT1jw7VYeeuhh\n/7PheZHAwEBMbhRPdQWr3V4tMzldgBZjGZVr/IWQkBAK3XyaKI9QfQAFBQVe689r+FmAyU2vmAFu\nv/12Tp69uvLIqnfGs3DZZjb9csijPmMjw8jOcU8xT31hCv3vbEWdWt4vqWM0mVm4fBuPPDrK6337\nEwGBgV7xxLiSxVsO0ampey5cNru90smMdAEav1fMn326kGZ1vedFFBkcSE6OX1WacyBUrm1VxP+E\nYtbpdOQVFLH/yJnLM6OGiXEM7NaKuR9/73Z/UkoW/ecnpHTvDhoXF8ua7YdocPckRk35mM+/+4nz\nORcrvtAFXv1oFW3btb+pzRgAF/PzCdd7191q9b6T7DpyltlLNrN060FyCwwVXmOx2iudpwP81ebq\n4Ndff+WN1+fyrzHdvNZnx0ZxbFy3xmv9eQfhNcVcRvHqKCHEOiHEUee+Qt/H/wnFXK9ePUb+5VEG\nPruQpH7TeOq1r9m06widWzfmxJnzbveXk1fI59/9xPKV37l13YuzXubsuQzWrN9Eqy4D+OrHU6Tc\n8xytBk7l6Vc+Z9Xm/RQVu//YeOJMFm8tWscbb73j9rU3GseOH68wY5y7fP7EPbRvGM/ybYcY/cYK\npn62scJrbHZ7mX7urlJitqDXB1XcsBowm80Mf2gws4d1pk5sxT77rtK7dRKr167FZvPuU0+lEHhz\nxrwAuDayaDLwg5SyEfCD87hcXMnH/DFwD5AlpWzmPPcAMA1IAdpJKUtdBRNC9ATeBNQ48jTPrmg8\nX6BWq3nl1bnMeeVVfvvtN5Yt+5ZJ73zD74cOo9cF8NE3m7ijbQoNEuNcClsN0GpQqVTExsa6LYsQ\nguTkZJKTk5kwYQJWq5Vdu3axbt1aXl20hkFPzad1syS6tW9Mt45NadusAZpyZmY2m51RL3zCs5Of\n88+IKi+SnZ3NwT+OM+YjC0Jwlf/5Q7c25tn+nnmj3N8xmfs7JgMw/oO1LNt+iPiIYNQqFUKASgjM\nVhsFBhMFBhOFBhO/HDlH4wa1Kui5fAxGM0Eu5lquamZMn0rtEBjZrYVX+02MDaN2TBgrVqxwqWxW\n1eEd+3FpxauB/jhy2gMsBDYBz1IOrnhlLADeAT694twB4D7gvbIuEkKogfnAXcBZYKcQYoWU8qAL\nY/oEIQQtWrSgRYsWvPDCVI4dO8Znn33KxgOHmTr/FdQCurRNpkvbRuUq6gCtBrOXErFoNBo6dOhA\nhw4dmDLlBYqLi9myZQvr1q5h3Mtfc+rP03Rul0r3dk3o1rEpyQ2uLsz6+oJVSG0YEyc+7RV5/JmY\nmBj279+PxWJx5rpWoVKp2LFjB1++9zrP9q/8GNGhevKLjKz/9fRlpS+lRKtRExIUSHBQAKGhwTx4\nV2vu71q5dOMlJgtBQf43Y965cycf/N+/2PP6CJ/k13h9ZBcGj/oLF3JzeXSUP6yJ+HxhL15KmQEg\npcxw1kAtF1cS5V93B5BSHgIq+qO1A445E+YjhFiC485RbYr5WpKSkpg+3VHtSkrJ0aNH2bhxIxs3\n/lCuog7QajCZzD5JDBMcHEzPnj3p2dPxNJSVlcWGDRtYt3Y1r419C6vVTLcOTenWIZn46HBeW7iW\nnbv2XJfV7mbk0o31WrZu3UqdaO+40KUmRlMrJpwf33XNP70yGIwW9PrK5fnwNkajkREPD+GNR++k\nZpRv6j/e0bwum14cTK8pzxGkD2Lo0Id8Mo57uGyWihFCXGkheN9Zr9Sr+NKPOQE4c8XxWaB9WY19\nWSXbFYQQNG7cmMaNGzNmzBiklBw7doyNGzeyaeMPTHv3VYS006VdCl3aNAIcyfl9HcUUFxfH4MGD\nGTx4MFJKjh8/zrp161i5bjU/7VjGm2+9c9ObMCpCSonWC1VFLhSW8NK3vxARWjWzWH+Mgntt7quk\nxusZdLtvU8Um145hxfMDuGvCOOrVq0+nTp18Ol6FuO5hkyOldNdmdl4IUdM5W64JVOjO5ctfRWn/\nKWUuQfuiSnZlEELQqFEjGjVqxOjRoy8rxU2bNrFpw3pat2pZLTIlJSWRlJTE2LFjq3x8f6VFixZ8\n/FZ2xQ0roNdLS0moEcXi6cO8IFXFqFXCvxbBgD8OH6Rdw6oppNC8Xhwfj+/JgwMHcPDI0WqsqCPw\nsR/ECmAEMNu5X17RBb6U5iyQeMVxbSDdh+P5lEtKcdSoUfx78RJ27t7jd7Od/1U6duzI6ewCTmVV\nzvXw7IVCJg/vVmUzZo1ajbWM6MHq4omJzzDvP7spNnov2q88erdJokdaHZ6fXO5amO/xUoCJs3j1\nT0ATIcRZIcSjOBTyXUKIozjW3Cp0gvClYt4JNBJC1BdCBACDcdw5FBS8ikajYfiI4cxattPjPs7m\nFpBbYCAtqXKeFu6g0aj8rppHq1at6NzlDmYs2VZlY84Z3pmvv1zCzp2e//0qj3BxKx8p5RApZU0p\npVZKWVtK+ZGUMldK2U1K2ci5rzD1YIWKubQ7gBBigBDiLNAR+E4IscbZtpYQ4nungFZgPLAGOAR8\nKaX8vcJPpqDgAS9Mm8HqX8/y4+9nKm58Dc8v3kzKEx8zok97wkOqzksiMjSIvHzvlMfyJm/N/z+W\n70vnjeVVoyijQoOYOeRWnnp8fPUE3AhuvJDsMu4A3zpfB0op46WUPZxt06WUva+49nspZWMpZUMp\nZflJkhUUKkF4eDgffLyAh+ev4Wyue0Vav9pxjLefGsh7z3hejswTYiNCyMmtOG9zVRMfH8/6jT8y\n7/v9LN/hfrV4TxjZrQU5GWfYtq3qZur/xdXZslKMVUHBbXr37s34Jyby4BsfsOmFgQRoXXMhNFos\n1K8VVamxLVYbJSYLJSYLBqP58mvHsYUS83/fMzrPXywykp17oVrr8ZVFnTp1+Prb5fTt3YPk2jE0\nqe39/C5Xolar6NumAevWruW2227z6VilIvzL3VRRzAo3Fc9Ofo6ftm5h0qItTOxzC0aLFZPFUena\naLZistocxxYrRovjdYnJwn+2HeSn3045lKfJRonZSonZhsFkocRkdSjWqxSuGUOJiRKTmRKjCbtd\nog/SEaQLRK8PIkinIyhIhz5IT1BQkGPT69Hrg9EFhTle1w7hjTfu9TulfIn27dvz4ktzGDh7Opte\nHExMmG99rjsl1+K97Vt8OkaZ+NnfQFHMCjcVKpWKhYs+5+477+D2md+iCwwkMCCAwMBAAgMD0ekC\nna916HQ6AnXBNE9rRUl4Yy6GhaPXB1ND/19lqnfhdVBQEFqt1m8VbGUYPWYMf/55krqPzqNGdDg1\no8KIiwgmLEhLqE5DaKCGEJ2GyJAgakaFUCsqhFpRoSREh7r9fWg1Kuz2qqlAfjVVa6ZwBUUxK9x0\nREZGsnPv/uoW46Zh1kuz+cc/XyAjI4PMzEzOnz9PQUEBhYWFFBUVUVBwkdPZWazZc5b09EOcTc/A\narXQPrkOtSKDkUhnXhMcYe7S+dqZ60Q6z2fkXiQ6sVH1fEg/u6kqillBQaFC9Ho9DRs2pGHDhi61\nP3fuHDt27CA3N/eqvCalbVe+n5rq24jDslEUs4KCwk1OQkICAwcOrG4xXEeZMSsoKCj4EUIpxqqg\noKDghyiKWUFBQcG/UEwZCgoKCv6GopgVFBQU/AjFj1lBQUHB7xDK4p+CgoKCn6HYmCtm9+7dOUKI\nPyvZTQyQ4w15qhBF5qpBkblqqAqZvVRXTVHMFSKljK1sH0KIXR7U5qpWFJmrBkXmquGGkVnxY1ZQ\nUFDwR5QZs4KCgoJ/odiYq4z3q1sAD1BkrhoUmauGG0Rm/zNliGqpsaWgoKDgJ7S5JU3u2rLKpbYi\nJGF3VdjNb+YZs4KCgoJr+Jkpw7/m715CCBEhhPhaCHFYCHFICNGxumUqDyFEEyHEviu2AiHEE9Ut\nV0UIIZ4UQvwuhDgghPhcCKGrbpkqQgjxd6e8v/vrdyyE+FgIkSWEOHDFuSghxDohxFHnPrI6ZbyW\nMmR+wPk924UQfu6d4V/FWG9KxQy8CayWUiYDacChapanXKSUR6SULaWULYHWgAH4tprFKhchRALw\nONBGStkMUAODq1eq8hFCNAMeA9rh+F3cI4SoppIZ5bIA6HnNucnAD1LKRsAPzmN/YgHXy3wAuA/Y\nXOXSuIXTxuzKVkXcdIpZCBEGdAY+ApBSmqWU+dUrlVt0A45LKSsbYFMVaIAgIYQG0APp1SxPRaQA\nO6SUBimlFfgRGFDNMl2HlHIzcOGa0/2Bhc7XC4F7q1SoCihNZinlISnlkWoSyXUEimKuAhoA2cAn\nQoi9QogPhRDB1S2UGwwGPq9uISpCSnkOmAucBjKAi1LKtdUrVYUcADoLIaKFEHqgN5BYzTK5SryU\nMgPAuY+rZnluMvzLlHEzLv5pgFuACVLKn4UQb+J47JtSvWJVjBAiAOgHPFfdslSE08bZH6gP5ANf\nCSEellL+u3olKxsp5SEhxBxgHVAE7Aes1SuVQnWze8/+NSIoJsbF5lUSFn8zKuazwFkp5c/O46/x\nP3tcWfQC9kgpz1e3IC7QHTgppcwGEEIsBToBfquYAaSUH+E0cwkhXsLxe7kROC+EqCmlzBBC1ASy\nqlugmwUp5bW28WrnpjNlSCkzgTNCiCbOU92Ag9UokjsM4QYwYzg5DXQQQuiFEALH9+zXi6wAQog4\n574OjoWpG+X7XgGMcL4eASyvRlkUfMxNGWAihGgJfAgEACeAv0gp86pXqvJx2jzPAA2klBerWx5X\nEEJMBwbhMAfsBUZJKU3VK1X5CCG2ANGABXhKSvlDNYt0HUKIz4E7cGRnOw9MBZYBXwJ1cNwUH5BS\nXrtAWG2UIfMF4G0gFoe5a5+Uskd1yXgjcVMqZgUFBYUbmZvOlKGgoKBwo6MoZgUFBQU/Q1HMCgoK\nCn6GopgVFBQU/AxFMSsoKCj4GYpiVlBQUPAzFMWsoKCg4GcoillBQUHBz/h/LDzGZHqpfmcAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc35af9128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracts.plot(column='CRIME', cmap='OrRd', edgecolor='k', legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the 49 neighbourhoods are colored along a white-to-dark-red gradient, but the human eye can have a hard time comparing the color of shapes that are distant one to the other. In this case, it is especially hard to rank the peripheral districts colored in beige.\n",
    "\n",
    "Instead, we'll classify them in color bins."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification by quantiles\n",
    ">QUANTILES will create attractive maps that place an equal number of observations in each class: If you have 30 counties and 6 data classes, you’ll have 5 counties in each class. The problem with quantiles is that you can end up with classes that have very different numerical ranges (e.g., 1-4, 4-9, 9-250)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-15T21:30:30.408917Z",
     "start_time": "2017-12-15T21:30:30.088920Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc24b22278>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Y9myZVgsFoKDgx2VAkJCiIuLIzCw6Qiqxx+cwyOjUjDqXbO7miw2Hv5mBxa7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc24ab8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Splitting the data in three shows some spatial clustering around the center\n",
    "tracts.plot(column='CRIME', scheme='quantiles', k=3, cmap='OrRd', edgecolor='k', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-15T21:28:00.376417Z",
     "start_time": "2017-12-15T21:27:57.039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc24ab8e80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J2ZyD7yyLrgkBbDod+LHwYheg1Gqjq5TU9+Q9u6KCFf/6Fwa7nUBgL9A9K7NN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\nJ6EKrIqKioqT+H/x/qL4/AodTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc24a41080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also see where the top and bottom halves are located\n",
    "tracts.plot(column='CRIME', scheme='quantiles', k=2, cmap='OrRd', edgecolor='k', legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification by equal intervals\n",
    ">EQUAL INTERVAL divides the data into equal size classes (e.g., 0-10, 10-20, 20-30, etc.) and works best on data that is generally spread across the entire range. CAUTION: Avoid equal interval if your data are skewed to one end or if you have one or two really large outlier values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-15T21:28:00.376417Z",
     "start_time": "2017-12-15T21:27:57.045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc24a0b4a8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VLomfnx8JPeLImP0spZU1+Gm1BHhpe2qIycjR0ioWfLmGQKM/JqOeoAA9QQEG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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc249ca2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracts.plot(column='CRIME', scheme='equal_interval', k=4, cmap='OrRd', edgecolor='k', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-15T21:28:00.386417Z",
     "start_time": "2017-12-15T21:27:57.048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc28468588>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eq48ZEbM51Du9JCR0vyUfS5TAKroMs9lMVlYolKq99btmvzqHeZ9/gcPhpMnh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc284da550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No legend here as we'd be out of space\n",
    "tracts.plot(column='CRIME', scheme='equal_interval', k=12, cmap='OrRd', edgecolor='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classificaton by natural breaks\n",
    ">NATURAL BREAKS is a kind of “optimal” classification scheme that finds class breaks that will minimize within-class variance and maximize between-class differences. One drawback of this approach is each dataset generates a unique classification solution, and if you need to make comparison across maps, such as in an atlas or a series (e.g., one map each for 1980, 1990, 2000) you might want to use a single scheme that can be applied across all of the maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-15T21:28:00.376417Z",
     "start_time": "2017-12-15T21:27:57.042Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc23709fd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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69uxJfsFhKiurCAjwJyjAnzsGdOOKTJehLFpEmJ8PVaa2B91uKwatWpnBXuAo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc24868630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare this to the previous 3-bin figure with quantiles\n",
    "tracts.plot(column='CRIME', scheme='fisher_jenks', k=3, cmap='OrRd', edgecolor='k', legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other classification schemes in PySAL\n",
    "\n",
    "Geopandas includes only the most used classifiers found in PySAL. In order to use the others, you will need to add them as additional columns to your GeoDataFrame.\n",
    "\n",
    ">The max-p algorithm determines the number of regions (p) endogenously based on a set of areas, a matrix of attributes on each area and a floor constraint. The floor constraint defines the minimum bound that a variable must reach for each region; for example, a constraint might be the minimum population each region must have. max-p further enforces a contiguity constraint on the areas within regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AREA</th>\n",
       "      <th>PERIMETER</th>\n",
       "      <th>COLUMBUS_</th>\n",
       "      <th>COLUMBUS_I</th>\n",
       "      <th>POLYID</th>\n",
       "      <th>NEIG</th>\n",
       "      <th>HOVAL</th>\n",
       "      <th>INC</th>\n",
       "      <th>CRIME</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>...</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>NSA</th>\n",
       "      <th>NSB</th>\n",
       "      <th>EW</th>\n",
       "      <th>CP</th>\n",
       "      <th>THOUS</th>\n",
       "      <th>NEIGNO</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Max_P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.309441</td>\n",
       "      <td>2.440629</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>80.467003</td>\n",
       "      <td>19.531</td>\n",
       "      <td>15.725980</td>\n",
       "      <td>2.850747</td>\n",
       "      <td>...</td>\n",
       "      <td>38.799999</td>\n",
       "      <td>44.070000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>POLYGON ((8.624129295349121 14.23698043823242,...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.259329</td>\n",
       "      <td>2.236939</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44.567001</td>\n",
       "      <td>21.232</td>\n",
       "      <td>18.801754</td>\n",
       "      <td>5.296720</td>\n",
       "      <td>...</td>\n",
       "      <td>35.619999</td>\n",
       "      <td>42.380001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>POLYGON ((8.252790451049805 14.23694038391113,...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.192468</td>\n",
       "      <td>2.187547</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>26.350000</td>\n",
       "      <td>15.956</td>\n",
       "      <td>30.626781</td>\n",
       "      <td>4.534649</td>\n",
       "      <td>...</td>\n",
       "      <td>39.820000</td>\n",
       "      <td>41.180000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>POLYGON ((8.653305053710938 14.00809001922607,...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.083841</td>\n",
       "      <td>1.427635</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>33.200001</td>\n",
       "      <td>4.477</td>\n",
       "      <td>32.387760</td>\n",
       "      <td>0.394427</td>\n",
       "      <td>...</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>40.520000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>POLYGON ((8.459499359130859 13.82034969329834,...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.488888</td>\n",
       "      <td>2.997133</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>23.225000</td>\n",
       "      <td>11.252</td>\n",
       "      <td>50.731510</td>\n",
       "      <td>0.405664</td>\n",
       "      <td>...</td>\n",
       "      <td>40.009998</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>POLYGON ((8.685274124145508 13.63951969146729,...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AREA  PERIMETER  COLUMBUS_  COLUMBUS_I  POLYID  NEIG      HOVAL  \\\n",
       "0  0.309441   2.440629          2           5       1     5  80.467003   \n",
       "1  0.259329   2.236939          3           1       2     1  44.567001   \n",
       "2  0.192468   2.187547          4           6       3     6  26.350000   \n",
       "3  0.083841   1.427635          5           2       4     2  33.200001   \n",
       "4  0.488888   2.997133          6           7       5     7  23.225000   \n",
       "\n",
       "      INC      CRIME      OPEN  ...            X          Y  NSA  NSB   EW  \\\n",
       "0  19.531  15.725980  2.850747  ...    38.799999  44.070000  1.0  1.0  1.0   \n",
       "1  21.232  18.801754  5.296720  ...    35.619999  42.380001  1.0  1.0  0.0   \n",
       "2  15.956  30.626781  4.534649  ...    39.820000  41.180000  1.0  1.0  1.0   \n",
       "3   4.477  32.387760  0.394427  ...    36.500000  40.520000  1.0  1.0  0.0   \n",
       "4  11.252  50.731510  0.405664  ...    40.009998  38.000000  1.0  1.0  1.0   \n",
       "\n",
       "    CP   THOUS  NEIGNO                                           geometry  \\\n",
       "0  0.0  1000.0  1005.0  POLYGON ((8.624129295349121 14.23698043823242,...   \n",
       "1  0.0  1000.0  1001.0  POLYGON ((8.252790451049805 14.23694038391113,...   \n",
       "2  0.0  1000.0  1006.0  POLYGON ((8.653305053710938 14.00809001922607,...   \n",
       "3  0.0  1000.0  1002.0  POLYGON ((8.459499359130859 13.82034969329834,...   \n",
       "4  0.0  1000.0  1007.0  POLYGON ((8.685274124145508 13.63951969146729,...   \n",
       "\n",
       "   Max_P  \n",
       "0      0  \n",
       "1      0  \n",
       "2      2  \n",
       "3      2  \n",
       "4      3  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def max_p(values, k):\n",
    "    \"\"\"\n",
    "    Given a list of values and `k` bins,\n",
    "    returns a list of their Maximum P bin number.\n",
    "    \"\"\"\n",
    "    from pysal.esda.mapclassify import Max_P_Classifier\n",
    "    binning = Max_P_Classifier(values, k=k)\n",
    "    return binning.yb\n",
    "\n",
    "tracts['Max_P'] = max_p(tracts['CRIME'].values, k=5)\n",
    "tracts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc23a79f98>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PKat3cYa+rMWNrtWeRN50F/DuiLyg+l+2bBmvvPIKAwcOpKSkBICHH36Yc84Jzf9tUAIr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc236ae518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracts.plot(column='Max_P', cmap='OrRd', edgecolor='k', categorical=True, legend=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
